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Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.


Papers
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Book ChapterDOI
18 Sep 2015
TL;DR: This paper presents an approach for aesthetic image classification based on Multiple Kernel Learning (MKL) method, which seeks for maximizing the classification performance without explicit feature selection steps.
Abstract: Aesthetic image classification aims at predicting the aesthetic quality of photos automatically, i.e. whether the photo elicits a high or low level of affection in a majority of people. To solve the problem, one challenge is to build features specific to image aesthetic perceptions, and another one is to build effective learning models to bridge the “semantic gap” between the emotion related concepts and the extracted visual features. In this paper, we present an approach for aesthetic image classification based on Multiple Kernel Learning (MKL) method, which seeks for maximizing the classification performance without explicit feature selection steps. The experiments are conducted on a large diverse database built from online photo sharing website, and the results demonstrated the advantages of MKL in terms of feature selection, classification performance, and interpretation, for the aesthetic image classification task.

1 citations

Journal ArticleDOI
TL;DR: This letter defines two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH) and proposes a merging kernel which is a linear combination of two kernels when employing Support Vector Machine.
Abstract: In this letter, we analyze the influence of motion and outof-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets. key words: frequency spectrum, cepstrum, multiple kernel learning

1 citations

Book ChapterDOI
04 Dec 2016
TL;DR: A new Hierarchical Bayesian Multiple Kernel Learning (HB-MKL) model is proposed to effectively fuse diverse types of features for action recognition and the effectiveness of the method is evaluated with the complementary features capturing both appearance and motion information from the videos on challenging human action datasets.
Abstract: Human action recognition is an area with increasing significance and has attracted much research attention over these years Fusing multiple features is intuitively an appropriate way to better recognize actions in videos, as single type of features is not able to capture the visual characteristics sufficiently However, most of the existing fusion methods used for action recognition fail to measure the contributions of different features and may not guarantee the performance improvement over the individual features In this paper, we propose a new Hierarchical Bayesian Multiple Kernel Learning (HB-MKL) model to effectively fuse diverse types of features for action recognition The model is able to adaptively evaluate the optimal weights of the base kernels constructed from different features to form a composite kernel We evaluate the effectiveness of our method with the complementary features capturing both appearance and motion information from the videos on challenging human action datasets, and the experimental results demonstrate the potential of HB-MKL for action recognition

1 citations

Journal Article
TL;DR: A multi-kernel S3VM optimization model based on Lp norm constraint is presented, and an improved Quasi-Newton method named subBFGS as well as a local search algorithm based on label switching in pair are used to solve non-smooth and non-convex problems respectively with respect to fm.
Abstract: Kernel method is an effective approach to solve the nonlinear pattern recognition problems in the field of machine learning. At present, multiple kernel method has become a new research focus. Compared with the traditional single kernel method, multiple kernel method is more flexible, more interpretable and has better generalization performance when dealing with heterogeneous, irregular and non-flat distribution samples. A multi-kernel S3VM optimization model based on Lp norm constraint is presented in this paper in accordance with kernel method of supervised learning. Such model has two sets of parameters including decision functions fm in reproducing kernel Hilbert space and weighted kernel combination coefficients, and inherits the non-smooth and non-convex properties from single-kernel based S3VM. A two-layer optimization procedure is adopted to optimize these two groups of parameters, and an improved Quasi-Newton method named subBFGS as well as a local search algorithm based on label switching in pair are used to solve non-smooth and non-convex problems respectively with respect to fm. Base kernels and manifold kernels are added into the multi-kernel framework to exploit the geometric properties of the data. Experimental results show that the proposed algorithm is effective and has excellent generation performance.

1 citations

Posted Content
TL;DR: In this article, a set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons.
Abstract: Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset's price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202244
202172
2020101
2019113
2018114